Integration of Authoritative and Volunteered Geographic Information for Updating Urban Mapping: Challenges and Potentials

Abstract

Abstract. This paper provides a bibliometric review between integration of authoritative and volunteered geographic information for the purpose of cartographic updating of urban mappings. The adopted methodology was through a bibliometric survey of the literature published by Web of Science and Science Direct. The period was evaluated from 2005 to 2020 and the keywords used were: integration of authoritative data, volunteered geographic information, VGI, large scale topographic mapping, Authoritative urban mapping. The number of publications found was small for the topic that deals with this integration, totalizing 14 articles at Web of Science and 23 at Science Direct. 38% of them were published in the International Journal of Geo Information (ISPRS), 16% in the International Journal of Geographical Information Science. 5% were published in the Cartography and Geographic Information Science and the Computer Geosciences respectively. The other 36% is shown in several other journals, approximately 3% each. Regarding the origin of publications, 25% are in Germany (University of Heidelberg), 14% in the UK (New Castle University), 13% in China (Wuhan University), 11% in Canada (Calgary University), and other countries show percentages between 3% and 5%. Among the research, areas are physical geography, remote sensing, computer science, information science, engineering, and public administration. Among themes addressed in the articles, potentials can be pointed out as existence of models which institutions can implement management of information received collaboratively, existence of several methodologies for quality control of this information so that they can be integrated into authoritative data that are called as data conflation. Methodologies for handling big data and semantic interoperability, as well as automation of processes. This data potential is not only on platforms such as OpenStreetMap, but also on data collected through scraping from social networks such as twitter, sites, and others. Among the challenges, there are still somethings to investigate regarding consideration of temporal, historic, political, and economic aspects, as well as the consideration of legal aspects. The integration of this volunteered geographic information is necessary, mainly in cities with economic and cultural difficulties to maintain their mapping up to date, as well as the difficulty of accessing information that allows access to authoritative data.

Conflating point of interest (POI) data: A systematic review of matching methods

“…We used three bibliography databases to search relevant papers, which are Web of Science 5 , Scopus 6 , and Google Scholar 7 . These three databases have been widely used by researchers to search for literature, and were also used in previous review articles (Biljecki & Ito, 2021;Fernandes et al, 2020;Vilches-Blázquez & Ramos, 2021). We then used keywords (including key phrases), wildcards, and boolean operators to construct queries to search through these three databases.…”

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

“…A literature review of approaches for validating POI data quality was conducted with the search terms "POI", "points-of-interest", "data quality", "assessment", "validation", and "methods" on the search engine Google Scholar and Web of Science, seeking publications published from year 2010 onwards. The search returned 40 studies, including 7 review papers or editorials [11][12][13][14][15][16][17]. In addition, to ensure that the review is as representative as possible, further validation approaches were obtained from previous review articles [16,17] and the most recent review by Fonte et al in 2017 [11].…”

Creation of a Multimodal Urban Transportation Network through Spatial Data Integration from Authoritative and Crowdsourced Data

“…After the matching process, the G C features that had no match (null matching) in G R are analyzed for a possible data fusion operation with G R . This operation is also called conflation in the literature [34,61,76,91,92]. Merging one dataset's exclusive data into the another allows complementing the data in the reference dataset and improving its coverage and completeness.…”